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DataOps, which focuses on automated tools throughout the ETL development cycle, responds to a huge challenge for data integration and ETL projects in general. ETL projects are increasingly based on agile processes and automated testing. Click to learn more about author Wayne Yaddow. The […].
DataOps, which focuses on automated tools throughout the ETL development cycle, responds to a huge challenge for data integration and ETL projects in general. ETL projects are increasingly based on agile processes and automated testing. Click to learn more about author Wayne Yaddow. The […].
They must put high-qualitydata into the hands of users as efficiently as possible. DataOps has emerged as an exciting solution. As the latest iteration in this pursuit of high-qualitydata sharing, DataOps combines a range of disciplines. People want to know how to implement DataOps successfully.
The ability to effectively deploy AI into production rests upon the strength of an organization’s data strategy because AI is only as strong as the data that underpins it. Organizations must support quality enhancement across structured, semistructured and unstructured data alike.
Automated Data Orchestration (AKA DataOps). Automated data orchestration interweaves data with connecting processes. DataOps is the leading process concept in data today. See Gartner’s “ How DataOps Amplifies Data and Analytics Business Value ”). Alation Data Catalog for the data fabric.
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